Bad debt and fraud... It's all the same, right?

The misclassification of bad debt and what it means for hidden fraud

By Carl Eastwood, Pre-Sales, SAS Australia & New Zealand

Analytics can and does play a significant role in fraud management within any business. Fraud teams are responsible for using varying levels of analytics to identify and prevent fraud. This is commonly done by using historical transactions/events to predict the likelihood of a future transaction/event being fraud. The use of historical fraud data is most applicable with credit/debit card or online banking fraud because the victim will notify the bank when they become aware that money has been taken out of their account. However, fraud can go undetected where a victim isn’t forthcoming.

Despite this hidden fraud, subscription fraud in telecommunications is now the number one fraud type globally, which demonstrates how much the fraudster’s value smartphones as much as they do a loan or credit card.

How much fraud is hidden in your bad debt losses?

In the case of application fraud in banking, or subscription fraud in telecommunications, a large proportion of fraud is typically misclassified as bad debt. For example, banking application fraud made up less than 1% ($1.3m) of reported fraud losses in Australia in 2015. This may be due to:

a) The victim doesn’t realise their identity has been stolen and used for many months

b) The fraudster is using their own identity or a variation of it

c) The fraudster is using fake details where there is no victim.

It is estimated that anywhere between 5-20% of bad debt is actually fraud, increasing to more than 50% where no payments have been made. A quick look at your bad debt population will give you an idea of the extent of the issue.

Despite this hidden fraud, subscription fraud in telecommunications is now the number one fraud type globally, which demonstrates how much the fraudster’s value smartphones as much as they do a loan or credit card.

What can analytics do?

The characteristics of fraudulent applications are different from someone who is simply a credit risk. It would then be pragmatic to assume that deploying different strategies to deal with these groups of customers would provide predictive uplifts. My own personal experience from working in Credit and Fraud Risk in the banking and telecommunications industry is that the predictive capabilities of analytics focused on fraud detection are significantly stronger than credit risk models and rules built using a mix of fraud and bad debt. Separating the strategies significantly reduce losses and also increase approval rates. Win-win!

There are various methods that you can use to detect hidden fraud. These can be reactive analytical techniques performed against the bad debt population in order to detect the fraud required to train business rules and models to predict future fraud. There are also proactive techniques that make it easier to detect suspicious applications and aid in the investigation process.

The hybrid analytics approach to fraud detection

Business rules and predictive models are essential analytical techniques that make up part of the SAS’ unique hybrid analytics approach. However, the use of anomaly detection or social network analysis can help detect fraud without the need to train rules and models on historic known frauds, cutting down on the work required to start uncovering that hidden fraud. SAS Fraud Framework for banking application fraud and telecommunications subscription fraud is one solution for uncovering and reducing hidden fraud losses. Whatever approach you take, you can always do more to detect fraud hidden in bad debt, the potential benefits in reduced losses and increased approval rates are huge.